app.py

import streamlit as st
from PIL import Image
import torch
from transformers import ViTForImageClassification, ViTImageProcessor
import logging
import base64
from io import BytesIO

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Load the model and feature extractor from Hugging Face
repository_id = "EnDevSols/brainmri-vit-model"
model = ViTForImageClassification.from_pretrained(repository_id)
feature_extractor = ViTImageProcessor.from_pretrained(repository_id)

# Function to perform inference
def predict(image):
    # Load and preprocess the image
    image = image.convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt")

    # Move the inputs to the appropriate device
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = {k: v.to(device) for k, v in inputs.items()}

    # Perform inference
    with torch.no_grad():
        outputs = model(**inputs)

    # Get the predicted label
    logits = outputs.logits
    predicted_label = logits.argmax(-1).item()

    # Map the label to "No" or "Yes"
    label_map = {0: "No", 1: "Yes"}
    diagnosis = label_map[predicted_label]

    # Return a complete statement
    if diagnosis == "Yes":
        return "The diagnosis indicates that you have a brain tumor."
    else:
        return "The diagnosis indicates that you do not have a brain tumor."

# Custom CSS
def set_css(style):
    st.markdown(f"<style>{style}</style>", unsafe_allow_html=True)

# Combined dark mode styles
combined_css = """
    .main, .sidebar .sidebar-content { background-color: #1c1c1c; color: #f0f2f6; }
    .block-container { padding: 1rem 2rem; background-color: #333; border-radius: 10px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5); }
    .stButton>button, .stDownloadButton>button { background: linear-gradient(135deg, #ff7e5f, #feb47b); color: white; border: none; padding: 10px 24px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px; margin: 4px 2px; cursor: pointer; border-radius: 5px; }
    .stSpinner { color: #4CAF50; }
    .title {
        font-size: 3rem;
        font-weight: bold;
        display: flex; 
        align-items: center; 
        justify-content: center;
    }
    .colorful-text {
        background: -webkit-linear-gradient(135deg, #ff7e5f, #feb47b);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
    }
    .black-white-text {
        color: black;
    }
    .small-input .stTextInput>div>input {
        height: 2rem;
        font-size: 0.9rem;
    }
    .small-file-uploader .stFileUploader>div>div {
        height: 2rem;
        font-size: 0.9rem;
    }
    .custom-text {
        font-size: 1.2rem;
        color: #feb47b;
        text-align: center;
        margin-top: -20px;
        margin-bottom: 20px;
    }
"""

# Streamlit application
st.set_page_config(layout="wide")

st.markdown(f"<style>{combined_css}</style>", unsafe_allow_html=True)

st.markdown('<div class="title"><span class="colorful-text">Brain MRI</span> <span class="black-white-text">Tumor Detection</span></div>', unsafe_allow_html=True)
st.markdown('<div class="custom-text">Upload an MRI image to detect brain tumor</div>', unsafe_allow_html=True)

# Uploading image
uploaded_file = st.file_uploader("Choose an image...", type="jpg")

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    
    # Resize the image for display
    resized_image = image.resize((150, 150))
    
    # Convert image to base64
    buffered = BytesIO()
    resized_image.save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue()).decode()

    # Display the image in the center
    st.markdown(f"<div style='text-align: center;'><img src='data:image/jpeg;base64,{img_str}' alt='Uploaded Image' width='300'></div>", unsafe_allow_html=True)
    
    st.write("")
    st.write("Result...")

    diagnosis = predict(image)
    st.write(diagnosis)